Sufficient dimension reduction based on an ensemble of minimum average variance estimators

TL;DR

This paper proposes a flexible ensemble method for sufficient dimension reduction that improves estimation accuracy without restrictive assumptions, applicable to various response types.

Contribution

It introduces a novel ensemble approach combining multiple functions to estimate the central subspace, enhancing flexibility and accuracy in dimension reduction.

Findings

Ensemble estimators achieve the same convergence rate as traditional methods.

They are consistent and applicable to both univariate and multivariate responses.

Performance comparisons show the ensemble method outperforms existing estimators.

Abstract

We introduce a class of dimension reduction estimators based on an ensemble of the minimum average variance estimates of functions that characterize the central subspace, such as the characteristic functions, the Box--Cox transformations and wavelet basis. The ensemble estimators exhaustively estimate the central subspace without imposing restrictive conditions on the predictors, and have the same convergence rate as the minimum average variance estimates. They are flexible and easy to implement, and allow repeated use of the available sample, which enhances accuracy. They are applicable to both univariate and multivariate responses in a unified form. We establish the consistency and convergence rate of these estimators, and the consistency of a cross validation criterion for order determination. We compare the ensemble estimators with other estimators in a wide variety of models, and establish their competent performance.